Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system....Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system.Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions.However,most of the previous fault diagnosis methods only used vibration or pressure signal,and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited.This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps.The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network.Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method.Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.展开更多
Introduction: Since the earliest description of spinal fusion in 1911 and later by Dr. Fred H. Albee, it has become one of the most commonly performed procedures by orthopedist and neurosurgeons. The spinal fusion is ...Introduction: Since the earliest description of spinal fusion in 1911 and later by Dr. Fred H. Albee, it has become one of the most commonly performed procedures by orthopedist and neurosurgeons. The spinal fusion is now used to treat a variety of indications, such as traumatic injuries, deformities, primary and secondary tumors, infections and degenerative conditions of the spine. The risk of iatrogenic injury during traditional anterior, posterior, and transforaminal open fusion surgery is significant. The axial lumbar interbody fusion (Axia-LIF) is a minimal invasive technique which uses the retroperitoneumpresacral anatomical corridor to fuse the lumbar vertebral bodies L4-L5-S1 avoiding manipulation of the annular ligament, paravertebral muscles and facet joints. Methods: In this retrospective series, we report all the cases made in the Centro Medico Naval in México City in two years. A total of eleven patients with degenerative disc disease and spondylolisthesis underwent Axia-LIF one or two level systems with a 36 months clinical and radiographic follow-up. The outcomes included Oswestry Disability Index (ODI) score and leg/back pain severity. Radiographic outcome was evaluated with dynamics and orthogonal x-ray, as well as lumbosacral tomography scan to evaluate fusion status. Results: Nine patients underwent Axia-LIF one level system (L5-S1) and the rest two levels system (L4-S1). Ten patients were fixated with transpedicular percutaneous screws and one with facets joints screws. No intraoperative complications were reported. The mean back pain severity improved 57% in 12 months, and the mean leg pain severity improved 50% in the same time (P < 0.001). Mean ODI scores improved 58%, from 60% ± 16% at baseline to 25% ± 8% at twelve months (P < 0.001). At one year, a patient developed pseudoarthrosis that required posterolateral arthrodesis with transpedicular percutaneous screws. At 36 months monitoring, 100% patients presented a total interbody fusion in the tomography scans. At final follow-up, mean O展开更多
Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images wit...Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhib展开更多
An axial piston pump is a key component that plays the role of the "heart" in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and...An axial piston pump is a key component that plays the role of the "heart" in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and catastrophic safety consequences. Several vibration-based machine learning methods have been developed to detect and diagnose faults of axial piston pumps. However,most of these intelligent diagnosis methods use single-sensor vibration data to monitor the pump health states. Additionally, the diagnostic accuracy is unacceptable in most situations due to the complex pump structure and limited sensor information.Therefore, this study proposes a multi-sensor fusion method to improve the fault diagnosis performance of axial piston pumps.The convolutional neural network receives three channels of vibration data and makes the final diagnosis through information fusion at the decision level. The proposed decision fusion method is evaluated on the classification task of leakage levels of an actual axial piston pump. The experimental results show that the proposed method improves the classification accuracy by adjusting the probability distribution of classification according to the learned weight matrix.展开更多
基金This study was supported by the National Key R&D Program of China(Grant No.2018YFB1702503)the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,China(Grant No.GZKF-202108)+2 种基金the National Postdoctoral Program for Innovative Talents,China(Grant No.BX20200210)the China Postdoctoral Science Foundation(Grant No.2019M660086)Shanghai Municipal Science and Technology Major Project,China(Grant No.2021SHZDZX0102).
文摘Axial piston pumps have wide applications in hydraulic systems for power transmission.Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system.Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions.However,most of the previous fault diagnosis methods only used vibration or pressure signal,and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited.This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps.The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network.Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method.Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.
文摘Introduction: Since the earliest description of spinal fusion in 1911 and later by Dr. Fred H. Albee, it has become one of the most commonly performed procedures by orthopedist and neurosurgeons. The spinal fusion is now used to treat a variety of indications, such as traumatic injuries, deformities, primary and secondary tumors, infections and degenerative conditions of the spine. The risk of iatrogenic injury during traditional anterior, posterior, and transforaminal open fusion surgery is significant. The axial lumbar interbody fusion (Axia-LIF) is a minimal invasive technique which uses the retroperitoneumpresacral anatomical corridor to fuse the lumbar vertebral bodies L4-L5-S1 avoiding manipulation of the annular ligament, paravertebral muscles and facet joints. Methods: In this retrospective series, we report all the cases made in the Centro Medico Naval in México City in two years. A total of eleven patients with degenerative disc disease and spondylolisthesis underwent Axia-LIF one or two level systems with a 36 months clinical and radiographic follow-up. The outcomes included Oswestry Disability Index (ODI) score and leg/back pain severity. Radiographic outcome was evaluated with dynamics and orthogonal x-ray, as well as lumbosacral tomography scan to evaluate fusion status. Results: Nine patients underwent Axia-LIF one level system (L5-S1) and the rest two levels system (L4-S1). Ten patients were fixated with transpedicular percutaneous screws and one with facets joints screws. No intraoperative complications were reported. The mean back pain severity improved 57% in 12 months, and the mean leg pain severity improved 50% in the same time (P < 0.001). Mean ODI scores improved 58%, from 60% ± 16% at baseline to 25% ± 8% at twelve months (P < 0.001). At one year, a patient developed pseudoarthrosis that required posterolateral arthrodesis with transpedicular percutaneous screws. At 36 months monitoring, 100% patients presented a total interbody fusion in the tomography scans. At final follow-up, mean O
基金supported in part by the National Natural Science Foundation of China under Grant 62062061/in part by the Major Project Cultivation Fund of Xizang Minzu University under Grant 324112300447.
文摘Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhib
基金supported by the National Key R&D Program of China(Grant No.2020YFB2007202)the National Natural Science Foundation of China(Grant No.52005323)+1 种基金the National Postdoctoral Program for Innovative Talents(Grant No.BX20200210)the China Postdoctoral Science Foundation(Grant No.2019M660086)。
文摘An axial piston pump is a key component that plays the role of the "heart" in hydraulic systems. The pump failure will lead to an unexpected breakdown of the entire hydraulic system or even economic loss and catastrophic safety consequences. Several vibration-based machine learning methods have been developed to detect and diagnose faults of axial piston pumps. However,most of these intelligent diagnosis methods use single-sensor vibration data to monitor the pump health states. Additionally, the diagnostic accuracy is unacceptable in most situations due to the complex pump structure and limited sensor information.Therefore, this study proposes a multi-sensor fusion method to improve the fault diagnosis performance of axial piston pumps.The convolutional neural network receives three channels of vibration data and makes the final diagnosis through information fusion at the decision level. The proposed decision fusion method is evaluated on the classification task of leakage levels of an actual axial piston pump. The experimental results show that the proposed method improves the classification accuracy by adjusting the probability distribution of classification according to the learned weight matrix.
